arXiv:2608. 07534v1 Announce Type: cross Abstract: Ultraconstructive Model Theory (UCMT) replaces idealized satisfaction, at finite compu- tational scale, by bounded adversarial survival.
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arXiv:2611. 06303v1 Announce Type: cross Abstract: The halting problem asks whether a program eventually halts, with unrestricted resources.
arXiv:2606. 13621v1 Announce Type: new Abstract: Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions.
arXiv:2605. 07032v2 Announce Type: replace-cross Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening.
The paper examines the vulnerability of open‑weight large language model safeguards that aim to block harmful usage by assuming new harmful behavior arises only through fine‑tuning. It demonstrates that simple, low‑cost attacks—abliteration and prefilling—can significantly increase attack success rates across multiple benchmarks, and proposes an abliteration‑resistant tuning (ART) method that mitigates these attacks. The study shows that safeguarding defenses must be evaluated against a broader range of attack strategies beyond fine‑tuning.
The paper investigates whether increased computational resources for an attacker can make adversarial attacks transferable across all defenders. It finds that for classification tasks, a quadratic resource gap guarantees either a transferable attack or a defense, whereas for generative tasks this guarantee fails. The authors also show that a defense that adds computation after seeing the input can be provably stronger than any fixed-model defense, suggesting that inference-time compute can enhance robustness.
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the wrong product.